Hydroelectric Unit Vibration Signal Feature Extraction Based on IMF Energy Moment and SDAE

Aiming at the problem that it is difficult to effectively characterize the operation status of hydropower units with a single vibration signal feature under the influence of multiple factors such as water–machine–electricity coupling, a multidimensional fusion feature extraction method for hydroelec...

Full description

Saved in:
Bibliographic Details
Published in:Water (Basel) 2024-07, Vol.16 (14), p.1956
Main Authors: Liu, Dong, Kong, Lijun, Yao, Bing, Huang, Tangming, Deng, Xiaoqin, Xiao, Zhihuai
Format: Article
Language:English
Subjects:
Analysis
Energy
Fault diagnosis
Fourier transforms
Hydroelectric power
Neural networks
Water-power
Wavelet transforms
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Aiming at the problem that it is difficult to effectively characterize the operation status of hydropower units with a single vibration signal feature under the influence of multiple factors such as water–machine–electricity coupling, a multidimensional fusion feature extraction method for hydroelectric units based on time–frequency analysis and unsupervised learning models is proposed. Firstly, the typical time–domain and frequency–domain characteristics of vibration signals are calculated through amplitude domain analysis and Fourier transform. Secondly, the time–frequency characteristics of vibration signals are obtained by combining the complementary ensemble empirical mode decomposition and energy moment calculation methods to supplement the traditional time–domain and frequency–domain characteristics, which have difficulty in comprehensively reflecting the correlation between nonlinear non–stationary signals and the state of the unit. Finally, in order to overcome the limitations of shallow feature extraction relying on artificial experience, a Stacked Denoising Autoencoder is used to adaptively mine the deep features of vibration signals, and the extracted features are fused to construct a multidimensional feature vector of vibration signals. The proposed multidimensional information fusion feature extraction method is verified to realize the multidimensional complementarity of feature attributes, which helps to accurately distinguish equipment state types and provides the foundation for subsequent state identification and trend prediction.
ISSN:2073-4441
2073-4441
DOI:10.3390/w16141956